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Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research. [ABSTRACT FROM AUTHOR]

Sheep are a key source of meat, milk and fibre for the global livestock sector, and an important biomedical model. Global analysis of gene expression across multiple tissues has aided genome annotation and supported functional annotation of mammalian genes. We present a large-scale RNA-Seq dataset representing all the major organ systems from adult sheep and from several juvenile, neonatal and prenatal developmental time points. The Ovis aries reference genome (Oar v3.1) includes 27,504 genes (20,921 protein coding), of which 25,350 (19,921 protein coding) had detectable expression in at least one tissue in the sheep gene expression atlas dataset. Network-based cluster analysis of this dataset grouped genes according to their expression pattern. The principle of ‘guilt by association’ was used to infer the function of uncharacterised genes from their co-expression with genes of known function. We describe the overall transcriptional signatures present in the sheep gene expression atlas and assign those signatures, where possible, to specific cell populations or pathways. The findings are related to innate immunity by focusing on clusters with an immune signature, and to the advantages of cross-breeding by examining the patterns of genes exhibiting the greatest expression differences between purebred and crossbred animals. This high-resolution gene expression atlas for sheep is, to our knowledge, the largest transcriptomic dataset from any livestock species to date. It provides a resource to improve the annotation of the current reference genome for sheep, presenting a model transcriptome for ruminants and insight into gene, cell and tissue function at multiple developmental stages. [ABSTRACT FROM AUTHOR]

Only a small proportion of genetic variation in serum ferritin has been explained by variant genetic studies, and genome-wide association study (GWAS) for serum ferritin has not been investigated widely in Chinese population. We aimed at exploring the novel genetic susceptibility to serum ferritin, and performed this two stage GWAS in a healthy Chinese population of 3,495 men aged 20–69 y, including 1,999 unrelated subjects in the first stage and 1,496 independent individuals in the second stage. Serum ferritin was measured with electrochemiluminescence immunoassay, and DNA samples were collected for genotyping. A total of 1,940,243 SNPs were tested by using multivariate linear regression analysis. After adjusting for population stratification, age and BMI, the rs5742933 located in the 5′UTR region of PMS1 gene on chromosome 2 was the most significantly associated with ferritin concentrations (P-combined = 2.329×10−10) (β = −0.11, 95% CI: −0.14, −0.07). Moreover, this marker was about 200kb away from the candidate gene SLC40A1 which is responsible for iron export. PMS1 gene was the novel genetic susceptibility to serum ferritin in Chinese males and its relation to SLC40A1 needs further study. [ABSTRACT FROM AUTHOR]